PharmaDS abstract

[fb-elong]

Abstract

Open source programming languages are rapidly transforming drug discovery, research, and development by offering powerful capabilities for study design, data analysis, visualization, and clinical reporting. The emergence of AI tools is also creating new opportunities. This workshop introduces practical strategies for using Python to prepare tables, listings, and figures (TLFs) in clinical study reports (CSRs), with a focus on how AI can accelerate the development lifecycle.

This workshop is designed for clinical programmers, statisticians, and data scientists interested in exploring Python as an alternative approach for clinical trial analysis and reporting. Participants will gain hands-on experience with reproducible workflows and AI in the loop. By the end of the session, attendees will have a clear roadmap for starting a Python project with AI for clinical trial analysis and reporting.

The workshop is based on the open source book Python for Clinical Study Reports and Submission and is organized into three modules:

• Python Environment Setup: Learn to use uv to create and manage reproducible Python projects, develop and collaborate in GitHub Codespaces, and get an overview of data processing in Python (e.g., polars and plotnine).
• Clinical Reporting: Take a guided tour of using Python for TLF creation commonly used in clinical trials and CSR project management.
• AI Applications: Explore how AI tools can be applied to clinical data analysis and trial design from a statistician’s perspective.

[fb-elong]

Yilong's Bio:

Yilong Zhang, PhD, is the Manager of the Health Quantitative Science team at Meta. He previously worked as a statistician at Merck & Co., Inc. on late-stage clinical trial development. His interests include developing R and Python packages to improve clinical trial analysis, reporting, and regulatory submission. Yilong has published over 25 peer-reviewed articles and holds a PhD in Biostatistics from New York University.

Profile picture: https://github.com/elong0527/elong0527.github.io/blob/master/static/image/yilong-profile.jpg

[eric-zhang16]

Eric's Bio:

Eric Zhang is the Director of Biostatistics at Eikon Therapeutics, where he supports oncology programs spanning early to late-stage clinical development. He earned his Ph.D. in Biostatistics from the University of Southern California and began his career at Merck, where he served as the lead statistician for late-stage oncology trials and played a pivotal role in the regulatory filing and approval of pembrolizumab (KEYTRUDA) for RCC and HN cancer. Following his time at Merck, Eric worked at BeiGene and Gilead Sciences. In addition to his project work, Eric is also an active participant in scientific discussions across industry and regulatory bodies, and has published extensively in both statistical and medical journals.

https://github.com/eric-zhang16.png

[xuyuting]

Yuting's bio:

Yuting Xu is a statistician at Merck & Co., where she supports preclinical development programs with innovative methodologies. She received her M.S.E. in Computer Science and Ph.D. in Biostatistics from Johns Hopkins University. Her professional interests include designing quantitative methods and implementing practical solutions to tackle complex challenges across the pharmaceutical R&D landscape.